Overview

Dataset statistics

Number of variables28
Number of observations56
Missing cells328
Missing cells (%)20.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.1 KiB
Average record size in memory221.6 B

Variable types

Numeric26
DateTime1
Categorical1

Alerts

df_index is highly correlated with SleepRegularityHigh correlation
Bedtime is highly correlated with TST and 4 other fieldsHigh correlation
TST is highly correlated with Bedtime and 5 other fieldsHigh correlation
WASO is highly correlated with NOA and 3 other fieldsHigh correlation
Waketime is highly correlated with Offset and 1 other fieldsHigh correlation
TIB is highly correlated with Bedtime and 5 other fieldsHigh correlation
NOA is highly correlated with WASO and 2 other fieldsHigh correlation
LSD is highly correlated with Bedtime and 5 other fieldsHigh correlation
DSD is highly correlated with BS and 1 other fieldsHigh correlation
REMSD is highly correlated with SS and 2 other fieldsHigh correlation
SS is highly correlated with TST and 5 other fieldsHigh correlation
MS is highly correlated with REMSD and 2 other fieldsHigh correlation
BS is highly correlated with DSD and 1 other fieldsHigh correlation
SE is highly correlated with WASO and 2 other fieldsHigh correlation
SMI is highly correlated with WASO and 3 other fieldsHigh correlation
AI is highly correlated with WASO and 2 other fieldsHigh correlation
REMP is highly correlated with REMSD and 1 other fieldsHigh correlation
SWSP is highly correlated with LSD and 2 other fieldsHigh correlation
Onset is highly correlated with Bedtime and 4 other fieldsHigh correlation
Offset is highly correlated with Waketime and 1 other fieldsHigh correlation
TSDP is highly correlated with Bedtime and 5 other fieldsHigh correlation
Midpoint is highly correlated with Waketime and 1 other fieldsHigh correlation
SleepRegularity is highly correlated with df_indexHigh correlation
df_index is highly correlated with SleepRegularityHigh correlation
Bedtime is highly correlated with TST and 5 other fieldsHigh correlation
TST is highly correlated with Bedtime and 5 other fieldsHigh correlation
WASO is highly correlated with NOA and 2 other fieldsHigh correlation
Waketime is highly correlated with Offset and 1 other fieldsHigh correlation
TIB is highly correlated with Bedtime and 5 other fieldsHigh correlation
NOA is highly correlated with WASO and 2 other fieldsHigh correlation
LSD is highly correlated with Bedtime and 5 other fieldsHigh correlation
DSD is highly correlated with BS and 1 other fieldsHigh correlation
REMSD is highly correlated with SS and 2 other fieldsHigh correlation
SS is highly correlated with Bedtime and 7 other fieldsHigh correlation
MS is highly correlated with REMSD and 2 other fieldsHigh correlation
BS is highly correlated with DSD and 1 other fieldsHigh correlation
SE is highly correlated with WASO and 2 other fieldsHigh correlation
SMI is highly correlated with WASO and 3 other fieldsHigh correlation
AI is highly correlated with NOA and 1 other fieldsHigh correlation
REMP is highly correlated with REMSD and 1 other fieldsHigh correlation
SWSP is highly correlated with LSD and 2 other fieldsHigh correlation
Onset is highly correlated with Bedtime and 5 other fieldsHigh correlation
Offset is highly correlated with Waketime and 1 other fieldsHigh correlation
TSDP is highly correlated with Bedtime and 5 other fieldsHigh correlation
Midpoint is highly correlated with Waketime and 2 other fieldsHigh correlation
IsWeekend is highly correlated with MidpointHigh correlation
SleepRegularity is highly correlated with df_indexHigh correlation
Bedtime is highly correlated with TST and 3 other fieldsHigh correlation
TST is highly correlated with Bedtime and 4 other fieldsHigh correlation
WASO is highly correlated with NOA and 1 other fieldsHigh correlation
Waketime is highly correlated with OffsetHigh correlation
TIB is highly correlated with Bedtime and 3 other fieldsHigh correlation
NOA is highly correlated with WASO and 1 other fieldsHigh correlation
DSD is highly correlated with BS and 1 other fieldsHigh correlation
REMSD is highly correlated with SS and 2 other fieldsHigh correlation
SS is highly correlated with TST and 2 other fieldsHigh correlation
MS is highly correlated with REMSD and 2 other fieldsHigh correlation
BS is highly correlated with DSD and 1 other fieldsHigh correlation
SMI is highly correlated with WASO and 1 other fieldsHigh correlation
AI is highly correlated with NOA and 1 other fieldsHigh correlation
REMP is highly correlated with REMSD and 1 other fieldsHigh correlation
SWSP is highly correlated with DSD and 1 other fieldsHigh correlation
Onset is highly correlated with Bedtime and 3 other fieldsHigh correlation
Offset is highly correlated with WaketimeHigh correlation
TSDP is highly correlated with Bedtime and 3 other fieldsHigh correlation
df_index is highly correlated with Date and 6 other fieldsHigh correlation
Date is highly correlated with df_index and 26 other fieldsHigh correlation
Bedtime is highly correlated with Date and 11 other fieldsHigh correlation
SOL is highly correlated with Date and 2 other fieldsHigh correlation
TST is highly correlated with Date and 12 other fieldsHigh correlation
WASO is highly correlated with df_index and 11 other fieldsHigh correlation
Waketime is highly correlated with Date and 7 other fieldsHigh correlation
TIB is highly correlated with Date and 12 other fieldsHigh correlation
NOA is highly correlated with df_index and 9 other fieldsHigh correlation
LSD is highly correlated with Date and 13 other fieldsHigh correlation
DSD is highly correlated with Date and 5 other fieldsHigh correlation
REMSD is highly correlated with Date and 8 other fieldsHigh correlation
ARR is highly correlated with df_index and 2 other fieldsHigh correlation
SS is highly correlated with Date and 9 other fieldsHigh correlation
MS is highly correlated with Date and 8 other fieldsHigh correlation
BS is highly correlated with Date and 2 other fieldsHigh correlation
SE is highly correlated with df_index and 15 other fieldsHigh correlation
SMI is highly correlated with Date and 13 other fieldsHigh correlation
AI is highly correlated with Date and 8 other fieldsHigh correlation
REMP is highly correlated with Date and 11 other fieldsHigh correlation
SWSP is highly correlated with Date and 8 other fieldsHigh correlation
Onset is highly correlated with Date and 13 other fieldsHigh correlation
Offset is highly correlated with Date and 7 other fieldsHigh correlation
TSDP is highly correlated with Date and 10 other fieldsHigh correlation
Midpoint is highly correlated with df_index and 16 other fieldsHigh correlation
Day is highly correlated with Date and 2 other fieldsHigh correlation
IsWeekend is highly correlated with Date and 3 other fieldsHigh correlation
SleepRegularity is highly correlated with df_index and 5 other fieldsHigh correlation
Bedtime has 14 (25.0%) missing values Missing
SOL has 14 (25.0%) missing values Missing
TST has 14 (25.0%) missing values Missing
WASO has 14 (25.0%) missing values Missing
Waketime has 14 (25.0%) missing values Missing
TIB has 14 (25.0%) missing values Missing
NOA has 14 (25.0%) missing values Missing
LSD has 14 (25.0%) missing values Missing
DSD has 14 (25.0%) missing values Missing
REMSD has 14 (25.0%) missing values Missing
ARR has 14 (25.0%) missing values Missing
SS has 14 (25.0%) missing values Missing
MS has 14 (25.0%) missing values Missing
BS has 14 (25.0%) missing values Missing
SE has 14 (25.0%) missing values Missing
SMI has 14 (25.0%) missing values Missing
AI has 14 (25.0%) missing values Missing
REMP has 14 (25.0%) missing values Missing
SWSP has 14 (25.0%) missing values Missing
Onset has 14 (25.0%) missing values Missing
Offset has 14 (25.0%) missing values Missing
TSDP has 14 (25.0%) missing values Missing
Midpoint has 14 (25.0%) missing values Missing
SleepRegularity has 6 (10.7%) missing values Missing
df_index is uniformly distributed Uniform
Day is uniformly distributed Uniform
df_index has unique values Unique
Date has unique values Unique
df_index has 1 (1.8%) zeros Zeros
IsWeekend has 40 (71.4%) zeros Zeros

Reproduction

Analysis started2022-11-29 10:10:36.908394
Analysis finished2022-11-29 10:12:07.417687
Duration1 minute and 30.51 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE
ZEROS

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.5
Minimum0
Maximum55
Zeros1
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:07.531399image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.75
Q113.75
median27.5
Q341.25
95-th percentile52.25
Maximum55
Range55
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation16.30950643
Coefficient of variation (CV)0.5930729611
Kurtosis-1.2
Mean27.5
Median Absolute Deviation (MAD)14
Skewness0
Sum1540
Variance266
MonotonicityNot monotonic
2022-11-29T10:12:07.707409image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
1.8%
11
 
1.8%
201
 
1.8%
211
 
1.8%
521
 
1.8%
221
 
1.8%
231
 
1.8%
241
 
1.8%
251
 
1.8%
261
 
1.8%
Other values (46)46
82.1%
ValueCountFrequency (%)
01
1.8%
11
1.8%
21
1.8%
31
1.8%
41
1.8%
51
1.8%
61
1.8%
71
1.8%
81
1.8%
91
1.8%
ValueCountFrequency (%)
551
1.8%
541
1.8%
531
1.8%
521
1.8%
511
1.8%
501
1.8%
491
1.8%
481
1.8%
471
1.8%
461
1.8%

Date
Date

HIGH CORRELATION
UNIQUE

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size576.0 B
Minimum2022-10-01 00:00:00
Maximum2022-11-25 00:00:00
2022-11-29T10:12:07.889405image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:12:08.068405image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Bedtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)100.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean23.52193122
Minimum21.44611111
Maximum26.335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:08.234373image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum21.44611111
5-th percentile22.28068055
Q122.96395834
median23.38222222
Q324.165
95-th percentile24.75118056
Maximum26.335
Range4.88888889
Interquartile range (IQR)1.201041668

Descriptive statistics

Standard deviation1.001098779
Coefficient of variation (CV)0.042560229
Kurtosis1.10699285
Mean23.52193122
Median Absolute Deviation (MAD)0.60013889
Skewness0.7312767807
Sum987.9211111
Variance1.002198766
MonotonicityNot monotonic
2022-11-29T10:12:08.401109image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
23.062777781
 
1.8%
26.163333331
 
1.8%
24.493888891
 
1.8%
24.308888891
 
1.8%
24.673333331
 
1.8%
23.275833331
 
1.8%
22.672222221
 
1.8%
24.241666671
 
1.8%
23.836944441
 
1.8%
23.110277781
 
1.8%
Other values (32)32
57.1%
(Missing)14
25.0%
ValueCountFrequency (%)
21.446111111
1.8%
22.178611111
1.8%
22.279722221
1.8%
22.298888891
1.8%
22.337222221
1.8%
22.387222221
1.8%
22.571666671
1.8%
22.642222221
1.8%
22.672222221
1.8%
22.734722221
1.8%
ValueCountFrequency (%)
26.3351
1.8%
26.163333331
1.8%
24.755277781
1.8%
24.673333331
1.8%
24.671
1.8%
24.628611111
1.8%
24.50751
1.8%
24.493888891
1.8%
24.331666671
1.8%
24.308888891
1.8%

SOL
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct30
Distinct (%)71.4%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean23.57142857
Minimum5
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:08.565150image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7.05
Q111.25
median18.5
Q333.75
95-th percentile50.9
Maximum79
Range74
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation16.15042529
Coefficient of variation (CV)0.6851695576
Kurtosis2.032858108
Mean23.57142857
Median Absolute Deviation (MAD)9
Skewness1.335130339
Sum990
Variance260.8362369
MonotonicityNot monotonic
2022-11-29T10:12:08.705352image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
85
 
8.9%
183
 
5.4%
212
 
3.6%
172
 
3.6%
152
 
3.6%
52
 
3.6%
192
 
3.6%
102
 
3.6%
161
 
1.8%
271
 
1.8%
Other values (20)20
35.7%
(Missing)14
25.0%
ValueCountFrequency (%)
52
 
3.6%
71
 
1.8%
85
8.9%
102
 
3.6%
111
 
1.8%
121
 
1.8%
131
 
1.8%
152
 
3.6%
161
 
1.8%
172
 
3.6%
ValueCountFrequency (%)
791
1.8%
551
1.8%
511
1.8%
491
1.8%
461
1.8%
411
1.8%
401
1.8%
391
1.8%
361
1.8%
351
1.8%

TST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct40
Distinct (%)95.2%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean418.0952381
Minimum258
Maximum565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:08.867707image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum258
5-th percentile349.85
Q1387.75
median422
Q3444
95-th percentile464.85
Maximum565
Range307
Interquartile range (IQR)56.25

Descriptive statistics

Standard deviation49.90860404
Coefficient of variation (CV)0.1193713764
Kurtosis3.18452308
Mean418.0952381
Median Absolute Deviation (MAD)30
Skewness-0.05149622304
Sum17560
Variance2490.868757
MonotonicityNot monotonic
2022-11-29T10:12:09.019851image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
4552
 
3.6%
4442
 
3.6%
4291
 
1.8%
4521
 
1.8%
3711
 
1.8%
3741
 
1.8%
3401
 
1.8%
4281
 
1.8%
4651
 
1.8%
3661
 
1.8%
Other values (30)30
53.6%
(Missing)14
25.0%
ValueCountFrequency (%)
2581
1.8%
3401
1.8%
3491
1.8%
3661
1.8%
3711
1.8%
3741
1.8%
3781
1.8%
3791
1.8%
3821
1.8%
3851
1.8%
ValueCountFrequency (%)
5651
1.8%
5341
1.8%
4651
1.8%
4621
1.8%
4611
1.8%
4552
3.6%
4541
1.8%
4521
1.8%
4471
1.8%
4442
3.6%

WASO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)73.8%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean35.26190476
Minimum5
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:09.182966image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12.1
Q122.5
median33.5
Q343
95-th percentile68.05
Maximum104
Range99
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation19.1783154
Coefficient of variation (CV)0.5438820033
Kurtosis4.26140395
Mean35.26190476
Median Absolute Deviation (MAD)10
Skewness1.613842173
Sum1481
Variance367.8077816
MonotonicityNot monotonic
2022-11-29T10:12:09.325962image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
313
 
5.4%
443
 
5.4%
392
 
3.6%
432
 
3.6%
182
 
3.6%
382
 
3.6%
362
 
3.6%
332
 
3.6%
502
 
3.6%
281
 
1.8%
Other values (21)21
37.5%
(Missing)14
25.0%
ValueCountFrequency (%)
51
1.8%
81
1.8%
121
1.8%
141
1.8%
151
1.8%
182
3.6%
191
1.8%
201
1.8%
211
1.8%
221
1.8%
ValueCountFrequency (%)
1041
 
1.8%
911
 
1.8%
691
 
1.8%
502
3.6%
491
 
1.8%
461
 
1.8%
443
5.4%
432
3.6%
411
 
1.8%
392
3.6%

Waketime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)100.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean7.52728836
Minimum6.884722222
Maximum9.631111111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:09.479109image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum6.884722222
5-th percentile6.986236111
Q17.250138889
median7.3625
Q37.587291667
95-th percentile8.371347223
Maximum9.631111111
Range2.746388889
Interquartile range (IQR)0.3371527775

Descriptive statistics

Standard deviation0.5463621989
Coefficient of variation (CV)0.07258419936
Kurtosis5.92041869
Mean7.52728836
Median Absolute Deviation (MAD)0.18875
Skewness2.230070842
Sum316.1461111
Variance0.2985116524
MonotonicityNot monotonic
2022-11-29T10:12:09.638178image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
7.5294444451
 
1.8%
9.1883333331
 
1.8%
7.6605555561
 
1.8%
7.2838888891
 
1.8%
7.9316666671
 
1.8%
7.3508333331
 
1.8%
7.5055555561
 
1.8%
7.3751
 
1.8%
7.1452777781
 
1.8%
8.0686111111
 
1.8%
Other values (32)32
57.1%
(Missing)14
25.0%
ValueCountFrequency (%)
6.8847222221
1.8%
6.9416666671
1.8%
6.981
1.8%
7.1047222221
1.8%
7.1066666671
1.8%
7.1197222221
1.8%
7.1294444441
1.8%
7.1452777781
1.8%
7.1691666671
1.8%
7.1783333331
1.8%
ValueCountFrequency (%)
9.6311111111
1.8%
9.1883333331
1.8%
8.3766666671
1.8%
8.2702777781
1.8%
8.1552777781
1.8%
8.0686111111
1.8%
7.9316666671
1.8%
7.7597222221
1.8%
7.7391666671
1.8%
7.6605555561
1.8%

TIB
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct35
Distinct (%)83.3%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean477.8095238
Minimum314
Maximum606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:09.797576image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum314
5-th percentile404.1
Q1444.5
median484.5
Q3514.75
95-th percentile538.9
Maximum606
Range292
Interquartile range (IQR)70.25

Descriptive statistics

Standard deviation54.24346115
Coefficient of variation (CV)0.1135252825
Kurtosis1.082869109
Mean477.8095238
Median Absolute Deviation (MAD)36.5
Skewness-0.3431798492
Sum20068
Variance2942.353078
MonotonicityNot monotonic
2022-11-29T10:12:09.948621image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
4893
 
5.4%
5352
 
3.6%
4042
 
3.6%
4842
 
3.6%
5012
 
3.6%
5302
 
3.6%
5081
 
1.8%
4381
 
1.8%
4291
 
1.8%
4181
 
1.8%
Other values (25)25
44.6%
(Missing)14
25.0%
ValueCountFrequency (%)
3141
1.8%
4042
3.6%
4061
1.8%
4091
1.8%
4181
1.8%
4211
1.8%
4271
1.8%
4291
1.8%
4381
1.8%
4441
1.8%
ValueCountFrequency (%)
6061
1.8%
5801
1.8%
5391
1.8%
5371
1.8%
5352
3.6%
5302
3.6%
5221
1.8%
5191
1.8%
5171
1.8%
5081
1.8%

NOA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)19.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean5.30952381
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:10.092268image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.880426719
Coefficient of variation (CV)0.3541610861
Kurtosis-0.02402505015
Mean5.30952381
Median Absolute Deviation (MAD)1
Skewness-0.3345700731
Sum223
Variance3.536004646
MonotonicityNot monotonic
2022-11-29T10:12:10.208256image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
615
26.8%
37
12.5%
55
 
8.9%
75
 
8.9%
44
 
7.1%
92
 
3.6%
82
 
3.6%
12
 
3.6%
(Missing)14
25.0%
ValueCountFrequency (%)
12
 
3.6%
37
12.5%
44
 
7.1%
55
 
8.9%
615
26.8%
75
 
8.9%
82
 
3.6%
92
 
3.6%
ValueCountFrequency (%)
92
 
3.6%
82
 
3.6%
75
 
8.9%
615
26.8%
55
 
8.9%
44
 
7.1%
37
12.5%
12
 
3.6%

LSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct35
Distinct (%)83.3%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean212.2619048
Minimum90
Maximum311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:10.372937image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile155.4
Q1191.25
median208.5
Q3232
95-th percentile269.55
Maximum311
Range221
Interquartile range (IQR)40.75

Descriptive statistics

Standard deviation40.5114013
Coefficient of variation (CV)0.1908557324
Kurtosis1.634383696
Mean212.2619048
Median Absolute Deviation (MAD)21.5
Skewness-0.05970310413
Sum8915
Variance1641.173635
MonotonicityNot monotonic
2022-11-29T10:12:10.512080image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2322
 
3.6%
2202
 
3.6%
1872
 
3.6%
1922
 
3.6%
2612
 
3.6%
2032
 
3.6%
1932
 
3.6%
2141
 
1.8%
2171
 
1.8%
1861
 
1.8%
Other values (25)25
44.6%
(Missing)14
25.0%
ValueCountFrequency (%)
901
1.8%
1471
1.8%
1551
1.8%
1631
1.8%
1791
1.8%
1811
1.8%
1851
1.8%
1861
1.8%
1872
3.6%
1911
1.8%
ValueCountFrequency (%)
3111
1.8%
3051
1.8%
2701
1.8%
2612
3.6%
2531
1.8%
2521
1.8%
2511
1.8%
2461
1.8%
2341
1.8%
2322
3.6%

DSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)81.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean127.8809524
Minimum73
Maximum201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:10.663674image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile80.6
Q1112.25
median125
Q3145.75
95-th percentile173.5
Maximum201
Range128
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation27.30157003
Coefficient of variation (CV)0.2134920762
Kurtosis0.3675117235
Mean127.8809524
Median Absolute Deviation (MAD)16.5
Skewness0.3436772298
Sum5371
Variance745.3757259
MonotonicityNot monotonic
2022-11-29T10:12:10.828784image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1183
 
5.4%
1322
 
3.6%
1112
 
3.6%
1472
 
3.6%
1152
 
3.6%
1262
 
3.6%
1642
 
3.6%
1331
 
1.8%
2011
 
1.8%
1131
 
1.8%
Other values (24)24
42.9%
(Missing)14
25.0%
ValueCountFrequency (%)
731
1.8%
791
1.8%
801
1.8%
921
1.8%
951
1.8%
1021
1.8%
1031
1.8%
1051
1.8%
1112
3.6%
1121
1.8%
ValueCountFrequency (%)
2011
1.8%
1811
1.8%
1741
1.8%
1642
3.6%
1591
1.8%
1521
1.8%
1491
1.8%
1472
3.6%
1461
1.8%
1451
1.8%

REMSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct32
Distinct (%)76.2%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean77.42857143
Minimum26
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:10.991785image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile31.8
Q166.5
median77
Q392
95-th percentile113.85
Maximum133
Range107
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation23.89217941
Coefficient of variation (CV)0.3085705828
Kurtosis0.2623818722
Mean77.42857143
Median Absolute Deviation (MAD)14
Skewness-0.04718138304
Sum3252
Variance570.8362369
MonotonicityNot monotonic
2022-11-29T10:12:11.134959image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
753
 
5.4%
823
 
5.4%
723
 
5.4%
962
 
3.6%
872
 
3.6%
522
 
3.6%
782
 
3.6%
681
 
1.8%
951
 
1.8%
281
 
1.8%
Other values (22)22
39.3%
(Missing)14
25.0%
ValueCountFrequency (%)
261
1.8%
281
1.8%
311
1.8%
471
1.8%
491
1.8%
522
3.6%
551
1.8%
561
1.8%
601
1.8%
661
1.8%
ValueCountFrequency (%)
1331
1.8%
1261
1.8%
1141
1.8%
1111
1.8%
1061
1.8%
1011
1.8%
991
1.8%
962
3.6%
951
1.8%
931
1.8%

ARR
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct36
Distinct (%)85.7%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean13.92285714
Minimum13.25
Maximum15.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:11.289995image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum13.25
5-th percentile13.423
Q113.7025
median13.87
Q314.1325
95-th percentile14.399
Maximum15.19
Range1.94
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.3750270606
Coefficient of variation (CV)0.02693607043
Kurtosis2.466989636
Mean13.92285714
Median Absolute Deviation (MAD)0.215
Skewness1.102607739
Sum584.76
Variance0.1406452962
MonotonicityNot monotonic
2022-11-29T10:12:11.448823image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
13.753
 
5.4%
14.152
 
3.6%
13.822
 
3.6%
14.072
 
3.6%
13.882
 
3.6%
13.861
 
1.8%
13.331
 
1.8%
14.081
 
1.8%
13.251
 
1.8%
13.591
 
1.8%
Other values (26)26
46.4%
(Missing)14
25.0%
ValueCountFrequency (%)
13.251
1.8%
13.331
1.8%
13.421
1.8%
13.481
1.8%
13.551
1.8%
13.571
1.8%
13.581
1.8%
13.591
1.8%
13.651
1.8%
13.681
1.8%
ValueCountFrequency (%)
15.191
1.8%
14.851
1.8%
14.41
1.8%
14.381
1.8%
14.321
1.8%
14.311
1.8%
14.241
1.8%
14.161
1.8%
14.152
3.6%
14.141
1.8%

SS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)50.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean85.11904762
Minimum59
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:11.611802image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile75.1
Q181.25
median85.5
Q390
95-th percentile92.95
Maximum96
Range37
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation6.707121737
Coefficient of variation (CV)0.07879695467
Kurtosis4.297785158
Mean85.11904762
Median Absolute Deviation (MAD)4.5
Skewness-1.501309979
Sum3575
Variance44.985482
MonotonicityNot monotonic
2022-11-29T10:12:11.751387image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
855
 
8.9%
904
 
7.1%
813
 
5.4%
883
 
5.4%
913
 
5.4%
893
 
5.4%
922
 
3.6%
832
 
3.6%
792
 
3.6%
842
 
3.6%
Other values (11)13
23.2%
(Missing)14
25.0%
ValueCountFrequency (%)
591
 
1.8%
741
 
1.8%
751
 
1.8%
772
3.6%
792
3.6%
801
 
1.8%
813
5.4%
821
 
1.8%
832
3.6%
842
3.6%
ValueCountFrequency (%)
961
 
1.8%
941
 
1.8%
931
 
1.8%
922
3.6%
913
5.4%
904
7.1%
893
5.4%
883
5.4%
872
3.6%
861
 
1.8%

MS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)64.3%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean79.69047619
Minimum50
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:11.902423image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile55.35
Q173.75
median82.5
Q388.5
95-th percentile93.9
Maximum99
Range49
Interquartile range (IQR)14.75

Descriptive statistics

Standard deviation11.49016142
Coefficient of variation (CV)0.1441848759
Kurtosis0.3946954007
Mean79.69047619
Median Absolute Deviation (MAD)6.5
Skewness-0.8781306939
Sum3347
Variance132.0238095
MonotonicityNot monotonic
2022-11-29T10:12:12.053437image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
834
 
7.1%
903
 
5.4%
872
 
3.6%
852
 
3.6%
922
 
3.6%
772
 
3.6%
942
 
3.6%
712
 
3.6%
782
 
3.6%
802
 
3.6%
Other values (17)19
33.9%
(Missing)14
25.0%
ValueCountFrequency (%)
501
1.8%
531
1.8%
551
1.8%
621
1.8%
641
1.8%
661
1.8%
681
1.8%
691
1.8%
712
3.6%
731
1.8%
ValueCountFrequency (%)
991
 
1.8%
942
3.6%
922
3.6%
911
 
1.8%
903
5.4%
892
3.6%
872
3.6%
861
 
1.8%
852
3.6%
841
 
1.8%

BS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)50.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean86.42857143
Minimum72
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:12.188423image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile74.2
Q183.25
median86.5
Q390.75
95-th percentile94.95
Maximum98
Range26
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation5.873225138
Coefficient of variation (CV)0.06795467102
Kurtosis0.2144955193
Mean86.42857143
Median Absolute Deviation (MAD)3.5
Skewness-0.4441573926
Sum3630
Variance34.49477352
MonotonicityNot monotonic
2022-11-29T10:12:12.320296image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
844
 
7.1%
914
 
7.1%
884
 
7.1%
863
 
5.4%
853
 
5.4%
903
 
5.4%
833
 
5.4%
872
 
3.6%
812
 
3.6%
742
 
3.6%
Other values (11)12
21.4%
(Missing)14
25.0%
ValueCountFrequency (%)
721
 
1.8%
742
3.6%
781
 
1.8%
791
 
1.8%
812
3.6%
821
 
1.8%
833
5.4%
844
7.1%
853
5.4%
863
5.4%
ValueCountFrequency (%)
981
 
1.8%
961
 
1.8%
951
 
1.8%
942
3.6%
931
 
1.8%
921
 
1.8%
914
7.1%
903
5.4%
891
 
1.8%
884
7.1%

SE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct41
Distinct (%)97.6%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean87.52214286
Minimum77.08
Maximum96.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:12.474467image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum77.08
5-th percentile81.904
Q185.42
median87.815
Q389.4475
95-th percentile93.388
Maximum96.2
Range19.12
Interquartile range (IQR)4.0275

Descriptive statistics

Standard deviation3.734642397
Coefficient of variation (CV)0.04267082906
Kurtosis0.7401946953
Mean87.52214286
Median Absolute Deviation (MAD)2.105
Skewness-0.1548241864
Sum3675.93
Variance13.94755383
MonotonicityNot monotonic
2022-11-29T10:12:12.647366image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
85.712
 
3.6%
89.571
 
1.8%
88.621
 
1.8%
86.481
 
1.8%
89.471
 
1.8%
84.161
 
1.8%
88.431
 
1.8%
87.741
 
1.8%
87.211
 
1.8%
84.541
 
1.8%
Other values (31)31
55.4%
(Missing)14
25.0%
ValueCountFrequency (%)
77.081
1.8%
81.031
1.8%
81.891
1.8%
82.171
1.8%
83.771
1.8%
84.051
1.8%
84.161
1.8%
84.431
1.8%
84.541
1.8%
85.051
1.8%
ValueCountFrequency (%)
96.21
1.8%
93.561
1.8%
93.391
1.8%
93.351
1.8%
93.231
1.8%
92.071
1.8%
91.051
1.8%
90.421
1.8%
89.781
1.8%
89.571
1.8%

SMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)100.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean91.59987675
Minimum78.23470411
Maximum98.17158931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:12.834227image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum78.23470411
5-th percentile84.2258906
Q190.25370832
median91.80815914
Q394.59567142
95-th percentile96.39278025
Maximum98.17158931
Range19.9368852
Interquartile range (IQR)4.3419631

Descriptive statistics

Standard deviation3.971634268
Coefficient of variation (CV)0.04335851104
Kurtosis2.482145407
Mean91.59987675
Median Absolute Deviation (MAD)1.884034441
Skewness-1.186582767
Sum3847.194824
Variance15.77387876
MonotonicityNot monotonic
2022-11-29T10:12:12.998247image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
93.429158131
 
1.8%
97.708081981
 
1.8%
87.914691951
 
1.8%
90.447400251
 
1.8%
95.371668951
 
1.8%
90.200210711
 
1.8%
91.355599191
 
1.8%
92.893401061
 
1.8%
90.414201121
 
1.8%
88.932419221
 
1.8%
Other values (32)32
57.1%
(Missing)14
25.0%
ValueCountFrequency (%)
78.234704111
1.8%
82.215743421
1.8%
84.17618271
1.8%
85.170340671
1.8%
87.914691951
1.8%
88.932419221
1.8%
89.0173411
1.8%
89.254598241
1.8%
89.922480621
1.8%
89.925768781
1.8%
ValueCountFrequency (%)
98.171589311
1.8%
97.708081981
1.8%
96.404494331
1.8%
96.170212791
1.8%
95.762711811
1.8%
95.586380881
1.8%
95.371668951
1.8%
95.145631061
1.8%
95.124593731
1.8%
95.094339631
1.8%

AI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct35
Distinct (%)83.3%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean0.7692857143
Minimum0.15
Maximum1.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:13.187916image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.401
Q10.5325
median0.8
Q30.965
95-th percentile1.1695
Maximum1.27
Range1.12
Interquartile range (IQR)0.4325

Descriptive statistics

Standard deviation0.2839047664
Coefficient of variation (CV)0.3690498356
Kurtosis-0.6600838039
Mean0.7692857143
Median Absolute Deviation (MAD)0.23
Skewness-0.282510395
Sum32.31
Variance0.08060191638
MonotonicityNot monotonic
2022-11-29T10:12:13.343727image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.793
 
5.4%
0.813
 
5.4%
0.422
 
3.6%
1.122
 
3.6%
1.172
 
3.6%
0.471
 
1.8%
0.171
 
1.8%
0.541
 
1.8%
0.871
 
1.8%
0.151
 
1.8%
Other values (25)25
44.6%
(Missing)14
25.0%
ValueCountFrequency (%)
0.151
1.8%
0.171
1.8%
0.41
1.8%
0.422
3.6%
0.431
1.8%
0.441
1.8%
0.451
1.8%
0.461
1.8%
0.471
1.8%
0.531
1.8%
ValueCountFrequency (%)
1.271
1.8%
1.172
3.6%
1.161
1.8%
1.131
1.8%
1.122
3.6%
1.091
1.8%
1.031
1.8%
0.981
1.8%
0.971
1.8%
0.951
1.8%

REMP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)100.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean18.53571429
Minimum5.63
Maximum31.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:13.504058image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum5.63
5-th percentile7.7395
Q116.46
median19.095
Q321.4575
95-th percentile25.203
Maximum31.11
Range25.48
Interquartile range (IQR)4.9975

Descriptive statistics

Standard deviation5.083536863
Coefficient of variation (CV)0.274256324
Kurtosis1.23741906
Mean18.53571429
Median Absolute Deviation (MAD)2.595
Skewness-0.5775905269
Sum778.5
Variance25.84234704
MonotonicityNot monotonic
2022-11-29T10:12:14.484197image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
19.561
 
1.8%
31.111
 
1.8%
14.021
 
1.8%
20.051
 
1.8%
24.121
 
1.8%
22.21
 
1.8%
6.021
 
1.8%
16.391
 
1.8%
13.611
 
1.8%
19.161
 
1.8%
Other values (32)32
57.1%
(Missing)14
25.0%
ValueCountFrequency (%)
5.631
1.8%
6.021
1.8%
7.541
1.8%
11.531
1.8%
12.051
1.8%
13.611
1.8%
14.021
1.8%
14.291
1.8%
16.11
1.8%
16.391
1.8%
ValueCountFrequency (%)
31.111
1.8%
25.681
1.8%
25.261
1.8%
24.121
1.8%
23.711
1.8%
23.541
1.8%
23.011
1.8%
22.911
1.8%
22.21
1.8%
21.711
1.8%

SWSP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)100.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean30.66809524
Minimum19.95
Maximum43.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:14.647280image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum19.95
5-th percentile21.155
Q127.18
median31.175
Q334.7775
95-th percentile39.5125
Maximum43.02
Range23.07
Interquartile range (IQR)7.5975

Descriptive statistics

Standard deviation5.732200122
Coefficient of variation (CV)0.1869108622
Kurtosis-0.5834010547
Mean30.66809524
Median Absolute Deviation (MAD)3.765
Skewness-0.1583005643
Sum1288.06
Variance32.85811823
MonotonicityNot monotonic
2022-11-29T10:12:14.806278image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
33.411
 
1.8%
32.591
 
1.8%
33.421
 
1.8%
28.071
 
1.8%
23.241
 
1.8%
34.351
 
1.8%
27.11
 
1.8%
19.951
 
1.8%
37.171
 
1.8%
36.121
 
1.8%
Other values (32)32
57.1%
(Missing)14
25.0%
ValueCountFrequency (%)
19.951
1.8%
20.671
1.8%
21.151
1.8%
21.251
1.8%
22.11
1.8%
22.971
1.8%
23.241
1.8%
24.011
1.8%
24.31
1.8%
27.061
1.8%
ValueCountFrequency (%)
43.021
1.8%
39.781
1.8%
39.611
1.8%
37.661
1.8%
37.51
1.8%
37.171
1.8%
36.121
1.8%
35.581
1.8%
35.011
1.8%
34.951
1.8%

Onset
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)100.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean23.91478836
Minimum21.74611111
Maximum26.46833333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:14.976240image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum21.74611111
5-th percentile22.71227778
Q123.41659722
median23.74944444
Q324.36979167
95-th percentile25.96983333
Maximum26.46833333
Range4.722222223
Interquartile range (IQR)0.9531944433

Descriptive statistics

Standard deviation0.9677682547
Coefficient of variation (CV)0.04046735602
Kurtosis1.144249102
Mean23.91478836
Median Absolute Deviation (MAD)0.3493055517
Skewness0.7761990499
Sum1004.421111
Variance0.9365753947
MonotonicityNot monotonic
2022-11-29T10:12:15.138908image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
23.412777781
 
1.8%
26.281
 
1.8%
24.627222221
 
1.8%
24.392222221
 
1.8%
25.991
 
1.8%
23.44251
 
1.8%
23.022222221
 
1.8%
24.808333341
 
1.8%
24.103611111
 
1.8%
23.560277781
 
1.8%
Other values (32)32
57.1%
(Missing)14
25.0%
ValueCountFrequency (%)
21.746111111
1.8%
22.529722221
1.8%
22.703888891
1.8%
22.871666671
1.8%
22.948888891
1.8%
22.995277781
1.8%
23.003888891
1.8%
23.022222221
1.8%
23.4051
1.8%
23.408888891
1.8%
ValueCountFrequency (%)
26.468333331
1.8%
26.281
1.8%
25.991
1.8%
25.586666671
1.8%
25.038611111
1.8%
24.808333341
1.8%
24.711944441
1.8%
24.640833331
1.8%
24.627222221
1.8%
24.498333341
1.8%

Offset
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)100.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean7.52728836
Minimum6.884722222
Maximum9.631111111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:15.303301image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum6.884722222
5-th percentile6.986236111
Q17.250138889
median7.3625
Q37.587291667
95-th percentile8.371347223
Maximum9.631111111
Range2.746388889
Interquartile range (IQR)0.3371527775

Descriptive statistics

Standard deviation0.5463621989
Coefficient of variation (CV)0.07258419936
Kurtosis5.92041869
Mean7.52728836
Median Absolute Deviation (MAD)0.18875
Skewness2.230070842
Sum316.1461111
Variance0.2985116524
MonotonicityNot monotonic
2022-11-29T10:12:15.454597image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
7.5294444451
 
1.8%
9.1883333331
 
1.8%
7.6605555561
 
1.8%
7.2838888891
 
1.8%
7.9316666671
 
1.8%
7.3508333331
 
1.8%
7.5055555561
 
1.8%
7.3751
 
1.8%
7.1452777781
 
1.8%
8.0686111111
 
1.8%
Other values (32)32
57.1%
(Missing)14
25.0%
ValueCountFrequency (%)
6.8847222221
1.8%
6.9416666671
1.8%
6.981
1.8%
7.1047222221
1.8%
7.1066666671
1.8%
7.1197222221
1.8%
7.1294444441
1.8%
7.1452777781
1.8%
7.1691666671
1.8%
7.1783333331
1.8%
ValueCountFrequency (%)
9.6311111111
1.8%
9.1883333331
1.8%
8.3766666671
1.8%
8.2702777781
1.8%
8.1552777781
1.8%
8.0686111111
1.8%
7.9316666671
1.8%
7.7597222221
1.8%
7.7391666671
1.8%
7.6605555561
1.8%

TSDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)100.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean456.75
Minimum306.5
Maximum587.4999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:15.625711image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum306.5
5-th percentile358.3750002
Q1423.6250002
median460.2499999
Q3486.8749999
95-th percentile516.4000001
Maximum587.4999999
Range280.9999998
Interquartile range (IQR)63.24999969

Descriptive statistics

Standard deviation53.40003768
Coefficient of variation (CV)0.1169130546
Kurtosis1.094304445
Mean456.75
Median Absolute Deviation (MAD)34.00000013
Skewness-0.3266015679
Sum19183.5
Variance2851.564024
MonotonicityNot monotonic
2022-11-29T10:12:15.793572image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
486.99999991
 
1.8%
414.50000021
 
1.8%
4221
 
1.8%
413.49999991
 
1.8%
356.50000021
 
1.8%
474.50000021
 
1.8%
509.00000021
 
1.8%
393.99999981
 
1.8%
422.50000031
 
1.8%
510.49999991
 
1.8%
Other values (32)32
57.1%
(Missing)14
25.0%
ValueCountFrequency (%)
306.51
1.8%
355.51
1.8%
356.50000021
1.8%
393.99999981
1.8%
396.49999981
1.8%
397.51
1.8%
4121
1.8%
413.49999991
1.8%
414.50000021
1.8%
4221
1.8%
ValueCountFrequency (%)
587.49999991
1.8%
5631
1.8%
516.50000011
1.8%
514.50000011
1.8%
510.49999991
1.8%
509.00000021
1.8%
499.99999991
1.8%
4991
1.8%
498.51
1.8%
495.00000011
1.8%

Midpoint
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)100.0%
Missing14
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean4.130257937
Minimum3.604166669
Maximum5.734166665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:15.973675image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3.604166669
5-th percentile3.652916668
Q13.828263888
median4.035416668
Q34.251041666
95-th percentile4.957583332
Maximum5.734166665
Range2.129999996
Interquartile range (IQR)0.4227777785

Descriptive statistics

Standard deviation0.4366779209
Coefficient of variation (CV)0.1057265497
Kurtosis3.465545474
Mean4.130257937
Median Absolute Deviation (MAD)0.2145833337
Skewness1.6634974
Sum173.4708333
Variance0.1906876066
MonotonicityNot monotonic
2022-11-29T10:12:16.131755image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
4.0583333321
 
1.8%
5.7341666651
 
1.8%
4.143888891
 
1.8%
3.8380555561
 
1.8%
4.9608333321
 
1.8%
3.9541666681
 
1.8%
4.2416666681
 
1.8%
4.0916666681
 
1.8%
3.6244444421
 
1.8%
4.2541666651
 
1.8%
Other values (32)32
57.1%
(Missing)14
25.0%
ValueCountFrequency (%)
3.6041666691
1.8%
3.6244444421
1.8%
3.6500000011
1.8%
3.7083333351
1.8%
3.7358333341
1.8%
3.7416666651
1.8%
3.76251
1.8%
3.8025000021
1.8%
3.8080555581
1.8%
3.8083333341
1.8%
ValueCountFrequency (%)
5.7341666651
1.8%
5.02251
1.8%
4.9608333321
1.8%
4.8958333321
1.8%
4.6916666671
1.8%
4.5969444461
1.8%
4.5491666671
1.8%
4.4911111111
1.8%
4.3041666681
1.8%
4.2875000011
1.8%

Day
Categorical

HIGH CORRELATION
UNIFORM

Distinct7
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size540.0 B
Monday
Tuesday
Wednesday
Thursday
Friday
Other values (2)
16 

Length

Max length9
Median length7
Mean length7.142857143
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaturday
2nd rowSunday
3rd rowMonday
4th rowTuesday
5th rowWednesday

Common Values

ValueCountFrequency (%)
Monday8
14.3%
Tuesday8
14.3%
Wednesday8
14.3%
Thursday8
14.3%
Friday8
14.3%
Saturday8
14.3%
Sunday8
14.3%

Length

2022-11-29T10:12:16.275264image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-29T10:12:16.379285image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
monday8
14.3%
tuesday8
14.3%
wednesday8
14.3%
thursday8
14.3%
friday8
14.3%
saturday8
14.3%
sunday8
14.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IsWeekend
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2857142857
Minimum0
Maximum1
Zeros40
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size352.0 B
2022-11-29T10:12:16.483270image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4558423058
Coefficient of variation (CV)1.59544807
Kurtosis-1.089622642
Mean0.2857142857
Median Absolute Deviation (MAD)0
Skewness0.974996043
Sum16
Variance0.2077922078
MonotonicityNot monotonic
2022-11-29T10:12:16.604950image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
040
71.4%
116
 
28.6%
ValueCountFrequency (%)
040
71.4%
116
 
28.6%
ValueCountFrequency (%)
116
 
28.6%
040
71.4%

SleepRegularity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct47
Distinct (%)94.0%
Missing6
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean0.401845315
Minimum0.1422780931
Maximum0.889335217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size576.0 B
2022-11-29T10:12:16.762419image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.1422780931
5-th percentile0.150398591
Q10.2214734754
median0.4097403968
Q30.5070227014
95-th percentile0.7547294699
Maximum0.889335217
Range0.7470571239
Interquartile range (IQR)0.2855492259

Descriptive statistics

Standard deviation0.2003516365
Coefficient of variation (CV)0.4985790031
Kurtosis-0.5694264946
Mean0.401845315
Median Absolute Deviation (MAD)0.170072649
Skewness0.5913074966
Sum20.09226575
Variance0.04014077826
MonotonicityNot monotonic
2022-11-29T10:12:16.929102image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.24100988352
 
3.6%
0.14227809312
 
3.6%
0.62768478892
 
3.6%
0.23635351921
 
1.8%
0.26942930951
 
1.8%
0.55511598291
 
1.8%
0.43158409921
 
1.8%
0.40566743681
 
1.8%
0.37204521691
 
1.8%
0.42342150741
 
1.8%
Other values (37)37
66.1%
(Missing)6
 
10.7%
ValueCountFrequency (%)
0.14227809312
3.6%
0.14314991621
1.8%
0.15925808241
1.8%
0.17604844671
1.8%
0.1930290421
1.8%
0.19510325011
1.8%
0.19548047481
1.8%
0.19781795881
1.8%
0.20357908771
1.8%
0.20431117411
1.8%
ValueCountFrequency (%)
0.8893352171
1.8%
0.77307049161
1.8%
0.75605848051
1.8%
0.75310512361
1.8%
0.73212571841
1.8%
0.73130783841
1.8%
0.71129708251
1.8%
0.62768478892
3.6%
0.57657854971
1.8%
0.55511598291
1.8%

Interactions

2022-11-29T10:12:02.063903image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:37.955145image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:41.033278image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:45.067870image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:48.344354image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:51.535315image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:54.883983image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:58.071694image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:01.322518image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:04.996146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:08.206275image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:11.528724image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:14.648923image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:18.697432image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:22.178211image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:25.486151image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:28.560915image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:32.464693image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:35.885182image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:39.038590image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:42.340228image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:45.606216image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:48.697102image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:52.538872image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:55.777443image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:58.883008image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:12:02.194134image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:38.073146image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:41.142202image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:45.181814image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:48.459682image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:51.916662image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:54.995354image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:58.187639image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:01.441835image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:05.103562image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-29T10:11:14.412419image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:18.418432image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:21.925103image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:25.237194image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:28.328251image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:32.203410image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:35.619362image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:38.793607image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:42.078560image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:45.360943image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:48.445342image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:52.283252image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:55.538218image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:58.645545image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:12:01.807716image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:12:05.211092image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:40.904191image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:44.932855image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:48.216222image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:51.417075image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:54.762077image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:10:57.946521image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:01.185201image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:04.867419image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:08.063074image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:11.398769image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:14.530420image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:18.555646image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:22.055716image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:25.365713image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:28.443069image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:32.334728image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:35.750356image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:38.908889image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:42.216268image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:45.475946image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:48.567407image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:52.403828image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:55.660212image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:11:58.761818image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T10:12:01.929772image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2022-11-29T10:12:17.128101image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T10:12:17.478135image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T10:12:17.822254image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T10:12:18.164031image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T10:12:05.495430image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T10:12:06.194389image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-29T10:12:06.654398image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-29T10:12:07.210530image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexDateBedtimeSOLTSTWASOWaketimeTIBNOALSDDSDREMSDARRSSMSBSSESMIAIREMPSWSPOnsetOffsetTSDPMidpointDayIsWeekendSleepRegularity
002022-10-0123.06277821.0455.032.07.529444508.06.0214.0152.089.013.8692.087.092.089.5793.4291580.7919.5633.4123.4127787.529444487.04.058333Saturday1NaN
112022-10-0222.38722219.0461.050.07.312222535.09.0252.0112.096.013.8292.094.083.086.1789.2545981.1720.8224.3022.7038897.312222516.54.304167Sunday1NaN
222022-10-0322.57166718.0447.031.06.980000504.06.0246.095.0106.014.1191.091.079.088.6991.8807810.8123.7121.2522.8716676.980000486.54.054167Monday00.143150
332022-10-0422.73472251.0411.022.06.884722489.03.0253.0126.031.014.1579.053.087.084.0593.8356160.447.5430.6623.5847226.884722438.03.650000Tuesday00.270951
442022-10-0524.5075008.0378.018.07.265833404.06.0185.0118.074.014.8585.083.085.093.5695.0943400.9519.5831.2224.6408337.265833397.53.953333Wednesday00.236354
552022-10-0622.27972215.0423.091.07.104722522.04.0220.0116.087.013.9581.082.084.081.0382.2157430.5720.5727.4222.5297227.104722514.54.287500Thursday00.241010
6422022-10-07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFriday00.241010
7432022-10-08NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSaturday10.269429
862022-10-0923.13472233.0443.038.07.759722517.06.0206.0141.096.014.3190.089.090.085.6991.4344690.8121.6731.8323.6847227.759722484.54.037500Sunday10.230007
972022-10-1023.72416713.0421.036.07.590833471.06.0192.0147.081.014.2488.080.091.089.3891.7211330.8619.2434.9223.9408337.590833459.03.825000Monday00.238326

Last rows

df_indexDateBedtimeSOLTSTWASOWaketimeTIBNOALSDDSDREMSDARRSSMSBSSESMIAIREMPSWSPOnsetOffsetTSDPMidpointDayIsWeekendSleepRegularity
46342022-11-1622.64222246.0444.039.07.475556530.04.0227.0102.0114.013.8485.092.081.083.7791.7355370.5425.6822.9723.4088897.475556484.04.033333Wednesday00.756058
47542022-11-17NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNThursday00.773070
48552022-11-18NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFriday00.889335
49352022-11-1924.67000055.0349.05.07.511667409.01.0163.0113.072.015.1977.086.084.085.3398.1715890.1720.6332.3825.5866677.511667355.54.549167Saturday10.301023
50362022-11-2023.52277819.0565.020.09.631111606.04.0230.0201.0133.014.0796.099.098.093.2396.1702130.4223.5435.5823.8394449.631111587.54.895833Sunday10.438619
51372022-11-2124.33166710.0379.014.07.106667406.03.0191.0118.069.013.5783.076.085.093.3595.5863810.4718.2131.1324.4983337.106667396.53.802500Monday00.470843
52382022-11-2223.39444412.0452.018.07.461111484.03.0232.0133.086.014.3893.087.088.093.3995.7627120.4019.0329.4223.5944447.461111472.03.933333Tuesday00.462013
53392022-11-2323.15694441.0429.015.07.256944486.03.0251.0103.075.013.7685.078.081.088.2796.4044940.4217.4824.0123.8402787.256944445.03.708333Wednesday00.518290
54402022-11-2423.75805620.0410.034.07.524722466.08.0229.0115.066.014.4087.080.084.087.9891.9282511.1716.1028.0524.0913897.524722446.03.808056Thursday00.487533
55412022-11-2523.21583318.0424.046.07.374167489.05.0187.0159.078.013.8288.084.093.086.7189.9257690.7118.4037.5023.5158337.374167471.53.929167Friday00.450635